Allocation of resources, which may include tangible resources (such as raw materials, component parts, equipment, and labor) or intangible resources (such as processing time and cost allocation), typically poses many challenges in commercial or other enterprises. Given the myriad ways in which available resources may be allocated in connection with activities of a particular enterprise, and the fact that resource allocation schemes may differ widely as to their efficacy (in terms of their ability to maximize output, profit, or other desired performance measures), complex systems for logistics planning have been developed. Such logistics planning systems have been advantageously employed in, for instance, the manufacture of a product within a factory.
As computer processing power and data storage have become increasingly affordable, and as algorithms for logistics planning have exploited this availability, many logistics planning capabilities for manufacturing and other environments have been implemented using computer-based techniques. In considering analysis of a manufacturing process for a particular product, for example, it may be necessary to account for one or more materials that will be employed in assembling, fabricating, processing, synthesizing, or in otherwise producing the product. However, previous logistics planning techniques have been suboptimal in that they have not typically employed intelligent methods to reduce the amount of computer processing required to attain a desired level of accuracy in modeling a particular workflow or estimating critical time points within a workflow. Since the availability of computer processing resources may significantly limit the speed with which workflow modeling tasks may be performed or may limit the complexity that may be considered in performing the tasks, previous techniques have been inadequate for many needs.